Title of Presentation: Autonomous Science Technologies for a Mars Rover
Autonomous Science Technologies for a Mars Rover
Primary (Corresponding) Author: Michele Judd
Organization of Primary Author: NASA Jet Propulsion Laboratory
Co-Authors: Tara Estlin, Daniel Gaines, Rebecca Castano, Benjamin Bornstein, Robert Anderson, and Issa Nesnas
Abstract: This paper presents technology for performing autonomous science and commanding for a planetary rover. The MER rovers have outperformed all expectations by lasting over 1100 sols (or Martian days), which is an order of magnitude longer than their original mission goal. The longevity of these vehicles will have significant effects on future mission goals, such as objectives for the Mars Science Laboratory rover mission (scheduled to fly in 2009) and the Astrobiology Field Lab rover mission (scheduled to potentially fly in 2016). Common objectives for future rover missions to Mars include the handling of opportunistic science, long-range or multi-sol driving, and onboard fault diagnosis and recovery. To handle these goals, a number of new technologies have been developed and integrated as part of the CLARAty architecture [Nesnas, et al., 2006]. CLARAty is a unified and reusable robotic architecture that was designed to simplify the integration, testing and maturation of robotic technologies for future missions. This paper focuses on technology comprising the CLARAty Decision Layer, which was designed to support and validate high-level autonomy technologies, such as automated planning and scheduling, onboard data analysis, task control executives, and model-based fault diagnosis and recovery.
One important goal of Decision Layer technologies for future rover missions is to support autonomous science. Systems for onboard data analysis and automated planning can enable this goal by analyzing gathered data onboard the rover (such as images gathered for navigation or science purposes), prioritizing data for downlink, and providing opportunistic data collection when new science opportunities are identified. This paper will in particular discuss how the CLARAty Decision Layer supports such capabilities and how they have been tested and demonstrated on various JPL research rovers.